{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started with Qiskit\n",
    "\n",
    "Here, we provide an overview of working with Qiskit.  The fundamental package of Qiskit is Terra that provides the basic building blocks necessary to program quantum computers. The fundamental unit of Qiskit is the [quantum circuit](https://en.wikipedia.org/wiki/Quantum_circuit). A basic workflow using Qiskit consists of two stages: **Build** and **Execute**. **Build** allows you to make different quantum circuits that represent the problem you are solving, and **Execute** that allows you to run them on different backends.  After the jobs have been run, the data is collected and postprocessed depending on the desired output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-31T05:04:39.026081Z",
     "start_time": "2021-07-31T05:04:36.903090Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from qiskit import *\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Circuit Basics <a name='basics'></a>\n",
    "\n",
    "\n",
    "### Building the circuit\n",
    "\n",
    "The basic element needed for your first program is the QuantumCircuit.  We begin by creating a `QuantumCircuit` comprised of three qubits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-31T05:04:39.030564Z",
     "start_time": "2021-07-31T05:04:39.028024Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create a Quantum Circuit acting on a quantum register of three qubits\n",
    "circ = QuantumCircuit(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After you create the circuit with its registers, you can add gates (\"operations\") to manipulate the registers. As you proceed through the tutorials you will find more gates and circuits; below is an example of a quantum circuit that makes a three-qubit GHZ state\n",
    "\n",
    "$$|\\psi\\rangle = \\left(|000\\rangle+|111\\rangle\\right)/\\sqrt{2}.$$\n",
    "\n",
    "To create such a state, we start with a three-qubit quantum register. By default, each qubit in the register is initialized to $|0\\rangle$. To make the GHZ state, we apply the following gates:\n",
    "- A Hadamard gate $H$ on qubit $q_{0}$, which puts it into the superposition state $\\left(|0\\rangle+|1\\rangle\\right)/\\sqrt{2}$.\n",
    "- A controlled-Not operation ($C_{X}$) between qubit $q_{0}$ and qubit $q_{1}$.\n",
    "- A controlled-Not operation between qubit $q_{0}$ and qubit $q_{2}$.\n",
    "\n",
    "On an ideal quantum computer, the state produced by running this circuit would be the GHZ state above.\n",
    "\n",
    "In Qiskit, operations can be added to the circuit one by one, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-31T05:04:39.584692Z",
     "start_time": "2021-07-31T05:04:39.573755Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7fa0f8fc9310>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a H gate on qubit $q_{0}$, putting this qubit in superposition.\n",
    "circ.h(0)\n",
    "# Add a CX (CNOT) gate on control qubit $q_{0}$ and target qubit $q_{1}$, putting\n",
    "# the qubits in a Bell state.\n",
    "circ.cx(0, 1)\n",
    "# Add a CX (CNOT) gate on control qubit $q_{0}$ and target qubit $q_{2}$, putting\n",
    "# the qubits in a GHZ state.\n",
    "circ.cx(0, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Circuit <a name='visualize'></a>\n",
    "\n",
    "You can visualize your circuit using Qiskit `QuantumCircuit.draw()`, which plots the circuit in the form found in many textbooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-31T05:04:40.867948Z",
     "start_time": "2021-07-31T05:04:40.271129Z"
    },
    "scrolled": true,
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uQmCC\n",
      "text/plain": [
       "<Figure size 267.197x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circ.draw('mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this circuit, the qubits are put in order, with qubit $q_{0}$ at the top and qubit $q_{2}$ at the bottom. The circuit is read left to right (meaning that gates that are applied earlier in the circuit show up further to the left)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "\n",
    "\n",
    "When representing the state of a multi-qubit system, the tensor order used in Qiskit is different than that used in most physics textbooks. Suppose there are $n$ qubits, and qubit $j$ is labeled as $Q_{j}$. Qiskit uses an ordering in which the $n^{\\mathrm{th}}$ qubit is on the <em><strong>left</strong></em> side of the tensor product, so that the basis vectors are labeled as  $Q_{n-1}\\otimes \\cdots  \\otimes  Q_1\\otimes Q_0$.\n",
    "\n",
    "For example, if qubit $Q_{0}$ is in state 0, qubit $Q_{1}$ is in state 0, and qubit $Q_{2}$ is in state 1, Qiskit would represent this state as $|100\\rangle$, whereas many physics textbooks would represent it as $|001\\rangle$.\n",
    "\n",
    "This difference in labeling affects the way multi-qubit operations are represented as matrices. For example, Qiskit represents a controlled-X ($C_{X}$) operation with qubit $Q_{0}$ being the control and qubit $Q_{1}$ being the target as\n",
    "\n",
    "$$C_X = \\begin{pmatrix} 1 & 0 & 0 & 0 \\\\  0 & 0 & 0 & 1 \\\\ 0 & 0 & 1 & 0 \\\\ 0 & 1 & 0 & 0 \\\\\\end{pmatrix}.$$\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating circuits using Qiskit Aer <a name='simulation'></a>\n",
    "\n",
    "Qiskit Aer is our package for simulating quantum circuits. It provides many different backends for doing a simulation. There is also a basic, Python only, implementation called `BasicAer` in Terra that can be used as a drop-in replacement for `Aer` in the examples below.\n",
    "\n",
    "### Statevector backend\n",
    "\n",
    "The most common backend in Qiskit Aer is the `statevector_simulator`. This simulator returns the quantum \n",
    "state, which is a complex vector of dimensions $2^n$, where $n$ is the number of qubits \n",
    "(so be careful using this as it will quickly get too large to run on your machine)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run the above circuit using the statevector simulator, first you need to import Aer and then set the backend to `statevector_simulator`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-31T05:04:43.436320Z",
     "start_time": "2021-07-31T05:04:43.290274Z"
    }
   },
   "outputs": [],
   "source": [
    "# Import Aer\n",
    "from qiskit import Aer\n",
    "\n",
    "# Run the quantum circuit on a statevector simulator backend\n",
    "backend = Aer.get_backend('statevector_simulator')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have chosen the backend, it's time to compile and run the quantum circuit. In Qiskit we provide the `run` method for this. `run` returns a `job` object that encapsulates information about the job submitted to the backend.\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Tip:</b> You can obtain the above parameters in Jupyter. Simply place the text cursor on a function and press Shift+Tab.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-31T05:04:44.311305Z",
     "start_time": "2021-07-31T05:04:44.306416Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create a Quantum Program for execution \n",
    "job = backend.run(circ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you run a program, a job object is made that has the following two useful methods: \n",
    "`job.status()` and `job.result()`, which return the status of the job and a result object, respectively.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> Jobs run asynchronously, but when the result method is called, it switches to synchronous and waits for it to finish before moving on to another task.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-31T05:04:45.848031Z",
     "start_time": "2021-07-31T05:04:45.844758Z"
    }
   },
   "outputs": [],
   "source": [
    "result = job.result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results object contains the data and Qiskit provides the method \n",
    "`result.get_statevector(circ)` to return the state vector for the quantum circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-31T05:04:46.702758Z",
     "start_time": "2021-07-31T05:04:46.697846Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.707+0.j 0.   +0.j 0.   +0.j 0.   +0.j 0.   +0.j 0.   +0.j 0.   +0.j\n",
      " 0.707+0.j]\n"
     ]
    }
   ],
   "source": [
    "outputstate = result.get_statevector(circ, decimals=3)\n",
    "print(outputstate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Qiskit also provides a visualization toolbox to allow you to view these results.\n",
    "\n",
    "Below, we use the visualization function to plot the real and imaginary components of the state density matrix $\\rho$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-31T05:04:48.212557Z",
     "start_time": "2021-07-31T05:04:47.532387Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.visualization import plot_state_city\n",
    "plot_state_city(outputstate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unitary backend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Qiskit Aer also includes a `unitary_simulator` that works _provided all the elements in the circuit are unitary operations_. This backend calculates the $2^n \\times 2^n$ matrix representing the gates in the quantum circuit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-31T05:04:49.855040Z",
     "start_time": "2021-07-31T05:04:49.843822Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.707+0.j  0.707-0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j\n",
      "   0.   +0.j  0.   +0.j]\n",
      " [ 0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j\n",
      "   0.707+0.j -0.707+0.j]\n",
      " [ 0.   +0.j  0.   +0.j  0.707+0.j  0.707-0.j  0.   +0.j  0.   +0.j\n",
      "   0.   +0.j  0.   +0.j]\n",
      " [ 0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.707+0.j -0.707+0.j\n",
      "   0.   +0.j  0.   +0.j]\n",
      " [ 0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.707+0.j  0.707-0.j\n",
      "   0.   +0.j  0.   +0.j]\n",
      " [ 0.   +0.j  0.   +0.j  0.707+0.j -0.707+0.j  0.   +0.j  0.   +0.j\n",
      "   0.   +0.j  0.   +0.j]\n",
      " [ 0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j\n",
      "   0.707+0.j  0.707-0.j]\n",
      " [ 0.707+0.j -0.707+0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j\n",
      "   0.   +0.j  0.   +0.j]]\n"
     ]
    }
   ],
   "source": [
    "# Run the quantum circuit on a unitary simulator backend\n",
    "backend = Aer.get_backend('unitary_simulator')\n",
    "job = backend.run(circ)\n",
    "result = job.result()\n",
    "\n",
    "# Show the results\n",
    "print(result.get_unitary(circ, decimals=3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OpenQASM backend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simulators above are useful because they provide information about the state output by the ideal circuit and the matrix representation of the circuit. However, a real experiment terminates by _measuring_ each qubit (usually in the computational $|0\\rangle, |1\\rangle$ basis). Without measurement, we cannot gain information about the state. Measurements cause the quantum system to collapse into classical bits. \n",
    "\n",
    "For example, suppose we make independent measurements on each qubit of the three-qubit GHZ state\n",
    "\n",
    "$$|\\psi\\rangle = (|000\\rangle +|111\\rangle)/\\sqrt{2},$$\n",
    "\n",
    "and let $xyz$ denote the bitstring that results. Recall that, under the qubit labeling used by Qiskit, $x$ would correspond to the outcome on qubit $q_{2}$, $y$ to the outcome on qubit $q_{1}$, and $z$ to the outcome on qubit $q_{0}$. \n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> This representation of the bitstring puts the most significant bit (MSB) on the left, and the least significant bit (LSB) on the right. This is the standard ordering of binary bitstrings. We order the qubits in the same way (qubit representing the MSB has index 0), which is why Qiskit uses a non-standard tensor product order.\n",
    "</div>\n",
    "\n",
    "Recall the probability of obtaining outcome $xyz$ is given by\n",
    "\n",
    "$$\\mathrm{Pr}(xyz) = |\\langle xyz | \\psi \\rangle |^{2}$$\n",
    "\n",
    "and as such for the GHZ state probability of obtaining 000 or 111 are both 1/2.\n",
    "\n",
    "To simulate a circuit that includes measurement, we need to add measurements to the original circuit above, and use a different Aer backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-31T05:05:39.326486Z",
     "start_time": "2021-07-31T05:05:39.315781Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌───┐           ░ ┌─┐      \n",
       "q_0: ┤ H ├──■────■───░─┤M├──────\n",
       "     └───┘┌─┴─┐  │   ░ └╥┘┌─┐   \n",
       "q_1: ─────┤ X ├──┼───░──╫─┤M├───\n",
       "          └───┘┌─┴─┐ ░  ║ └╥┘┌─┐\n",
       "q_2: ──────────┤ X ├─░──╫──╫─┤M├\n",
       "               └───┘ ░  ║  ║ └╥┘\n",
       "c: 3/═══════════════════╩══╩══╩═\n",
       "                        0  1  2 </pre>"
      ],
      "text/plain": [
       "     ┌───┐           ░ ┌─┐      \n",
       "q_0: ┤ H ├──■────■───░─┤M├──────\n",
       "     └───┘┌─┴─┐  │   ░ └╥┘┌─┐   \n",
       "q_1: ─────┤ X ├──┼───░──╫─┤M├───\n",
       "          └───┘┌─┴─┐ ░  ║ └╥┘┌─┐\n",
       "q_2: ──────────┤ X ├─░──╫──╫─┤M├\n",
       "               └───┘ ░  ║  ║ └╥┘\n",
       "c: 3/═══════════════════╩══╩══╩═\n",
       "                        0  1  2 "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Quantum Circuit\n",
    "meas = QuantumCircuit(3, 3)\n",
    "meas.barrier(range(3))\n",
    "# map the quantum measurement to the classical bits\n",
    "meas.measure(range(3), range(3))\n",
    "\n",
    "# The Qiskit circuit object supports composition using\n",
    "# the compose method.\n",
    "circ.add_register(meas.cregs[0])\n",
    "qc = circ.compose(meas)\n",
    "\n",
    "#drawing the circuit\n",
    "qc.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This circuit adds a classical register, and three measurements that are used to map the outcome of qubits to the classical bits. \n",
    "\n",
    "To simulate this circuit, we use the `qasm_simulator` in Qiskit Aer. Each run of this circuit will yield either the bitstring 000 or 111. To build up statistics about the distribution of the bitstrings (to, e.g., estimate $\\mathrm{Pr}(000)$), we need to repeat the circuit many times. The number of times the circuit is repeated can be specified in the `run` method, via the `shots` keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-31T05:06:23.358780Z",
     "start_time": "2021-07-31T05:06:23.338865Z"
    }
   },
   "outputs": [],
   "source": [
    "# Use Aer's qasm_simulator\n",
    "backend_sim = Aer.get_backend('qasm_simulator')\n",
    "\n",
    "# Execute the circuit on the qasm simulator.\n",
    "# We've set the number of repeats of the circuit\n",
    "# to be 1024, which is the default.\n",
    "job_sim = backend_sim.run(transpile(qc, backend_sim), shots=1024)\n",
    "\n",
    "# Grab the results from the job.\n",
    "result_sim = job_sim.result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have a result object, you can access the counts via the function `get_counts(circuit)`. This gives you the _aggregated_ binary outcomes of the circuit you submitted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-31T05:06:24.587309Z",
     "start_time": "2021-07-31T05:06:24.583432Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'111': 514, '000': 510}\n"
     ]
    }
   ],
   "source": [
    "counts = result_sim.get_counts(qc)\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Approximately 50 percent of the time, the output bitstring is 000. Qiskit also provides a function `plot_histogram`, which allows you to view the outcomes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-31T05:06:26.146850Z",
     "start_time": "2021-07-31T05:06:26.028680Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.visualization import plot_histogram\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The estimated outcome probabilities $\\mathrm{Pr}(000)$ and  $\\mathrm{Pr}(111)$ are computed by taking the aggregate counts and dividing by the number of shots (times the circuit was repeated). Try changing the `shots` keyword in the `run` method and see how the estimated probabilities change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-31T05:06:28.643646Z",
     "start_time": "2021-07-31T05:06:27.732825Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.19.0.dev0+be0ed5f</td></tr><tr><td><code>qiskit-aer</code></td><td>0.8.2</td></tr><tr><td><code>qiskit-ignis</code></td><td>0.6.0</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.16.0.dev0+4e20edc</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.7 (default, May  6 2020, 04:59:01) \n",
       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>6</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Sat Jul 31 01:06:28 2021 EDT</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2021.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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